{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import re\n",
    "import pickle\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "# from wordcloud import WordCloud, STOPWORDS\n",
    "from gensim.models.word2vec import Word2Vec\n",
    "from tqdm import tqdm_notebook as tqdm\n",
    "\n",
    "pd.set_option('display.max_seq_items', 100)\n",
    "pd.set_option('display.max_rows', 100)\n",
    "pd.set_option('max_colwidth', 200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# $$一、数据查看与初步处理$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = pd.read_csv('../../data/raw_data/train_set.csv')\n",
    "test_df = pd.read_csv('../../data/raw_data/test_set.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f4614a2cb38>"
      ]
     },
     "execution_count": 282,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_df['class'].value_counts().plot.bar(figsize=(10,6), fontsize=20, rot=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>article</th>\n",
       "      <th>word_seg</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>7368 1252069 365865 755561 1044285 129532 105340 856005 1010033 679500 426236 755561 426236 697742 1044285 678940 1007781 1205281 263861 195449 862363 1274033 427848 1040756 427495 72195 222667 12...</td>\n",
       "      <td>816903 597526 520477 1179558 1033823 758724 632718 422098 520477 414956 625597 1203094 441513 596474 660569 995362 924085 1278762 1155376 1109510 823120 1275770 1203094 520477 831818 970779 117955...</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  \\\n",
       "0   0   \n",
       "\n",
       "                                                                                                                                                                                                   article  \\\n",
       "0  7368 1252069 365865 755561 1044285 129532 105340 856005 1010033 679500 426236 755561 426236 697742 1044285 678940 1007781 1205281 263861 195449 862363 1274033 427848 1040756 427495 72195 222667 12...   \n",
       "\n",
       "                                                                                                                                                                                                  word_seg  \\\n",
       "0  816903 597526 520477 1179558 1033823 758724 632718 422098 520477 414956 625597 1203094 441513 596474 660569 995362 924085 1278762 1155376 1109510 823120 1275770 1203094 520477 831818 970779 117955...   \n",
       "\n",
       "   class  \n",
       "0     14  "
      ]
     },
     "execution_count": 283,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# article为字 word_seg为词向量\n",
    "train_df[:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "类别共有： 19\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>article</th>\n",
       "      <th>word_seg</th>\n",
       "      <th>class</th>\n",
       "      <th>c_numerical</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>7368 1252069 365865 755561 1044285 129532 105340 856005 1010033 679500 426236 755561 426236 697742 1044285 678940 1007781 1205281 263861 195449 862363 1274033 427848 1040756 427495 72195 222667 12...</td>\n",
       "      <td>816903 597526 520477 1179558 1033823 758724 632718 422098 520477 414956 625597 1203094 441513 596474 660569 995362 924085 1278762 1155376 1109510 823120 1275770 1203094 520477 831818 970779 117955...</td>\n",
       "      <td>14</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  \\\n",
       "0   0   \n",
       "\n",
       "                                                                                                                                                                                                   article  \\\n",
       "0  7368 1252069 365865 755561 1044285 129532 105340 856005 1010033 679500 426236 755561 426236 697742 1044285 678940 1007781 1205281 263861 195449 862363 1274033 427848 1040756 427495 72195 222667 12...   \n",
       "\n",
       "                                                                                                                                                                                                  word_seg  \\\n",
       "0  816903 597526 520477 1179558 1033823 758724 632718 422098 520477 414956 625597 1203094 441513 596474 660569 995362 924085 1278762 1155376 1109510 823120 1275770 1203094 520477 831818 970779 117955...   \n",
       "\n",
       "   class  c_numerical  \n",
       "0     14            7  "
      ]
     },
     "execution_count": 284,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 建立label的stoi表\n",
    "def create_lookup_table():\n",
    "    c_class_lst = list(train_df['class'].value_counts().index)\n",
    "    c_class_stoi = {c_class_lst[i]: i for i in range(len(c_class_lst))}\n",
    "    return c_class_stoi\n",
    "c_class_stoi = create_lookup_table()\n",
    "pickle.dump(c_class_stoi, open('../../data/label_stoi.pkl', 'wb'))\n",
    "c_numerical = [c_class_stoi[e] for e in train_df['class']]\n",
    "train_df['c_numerical'] = c_numerical\n",
    "train_df[:4]\n",
    "print('类别共有：', len(c_class_stoi))\n",
    "train_df[:1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# $$二、数据特征处理$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
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       "  Widgets Documentation</a> for setup instructions.\n",
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       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
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    {
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     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# 处理低频词\n",
    "def construct_dict(df, d_type='word'):\n",
    "    word_dict = {}\n",
    "    corput = df.word_seg if d_type == 'word' else df.article\n",
    "    for line in tqdm(corput):\n",
    "        for e in line.strip().split():\n",
    "            word_dict[e] = word_dict.get(e, 0) + 1\n",
    "    return word_dict\n",
    "# word_dict = construct_dict(train_df, d_type='word')\n",
    "char_dict = construct_dict(train_df, d_type='char')\n",
    "# word_stop_word = [e for e in word_dict if word_dict[e] <=2]\n",
    "char_stop_word = [e for e in char_dict if char_dict[e] <=2]\n",
    "# pickle.dump(set(word_stop_word), open('../../data/word_stopword.pkl', 'wb'))\n",
    "# pickle.dump(set(char_stop_word), open('../../data/char_stopword.pkl', 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 102277/102277 [00:21<00:00, 4669.26it/s]\n",
      "100%|██████████| 102277/102277 [00:32<00:00, 3178.19it/s]\n"
     ]
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    {
     "data": {
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       "<div>\n",
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       "    .dataframe tbody tr th {\n",
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>article</th>\n",
       "      <th>word_seg</th>\n",
       "      <th>class</th>\n",
       "      <th>c_numerical</th>\n",
       "      <th>word_mf2</th>\n",
       "      <th>char_mf2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>7368 1252069 365865 755561 1044285 129532 105340 856005 1010033 679500 426236 755561 426236 697742 1044285 678940 1007781 1205281 263861 195449 862363 1274033 427848 1040756 427495 72195 222667 12...</td>\n",
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       "      <td>14</td>\n",
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       "    </tr>\n",
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       "      <td>3</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  \\\n",
       "0   0   \n",
       "1   1   \n",
       "\n",
       "                                                                                                                                                                                                   article  \\\n",
       "0  7368 1252069 365865 755561 1044285 129532 105340 856005 1010033 679500 426236 755561 426236 697742 1044285 678940 1007781 1205281 263861 195449 862363 1274033 427848 1040756 427495 72195 222667 12...   \n",
       "1  581131 165432 7368 957317 1197553 570900 336592 7368 1030191 394259 230592 570900 7368 1155302 871922 94030 230478 1100129 1122654 538313 371412 313405 641588 952665 1044285 1043700 1007781 235615...   \n",
       "\n",
       "                                                                                                                                                                                                  word_seg  \\\n",
       "0  816903 597526 520477 1179558 1033823 758724 632718 422098 520477 414956 625597 1203094 441513 596474 660569 995362 924085 1278762 1155376 1109510 823120 1275770 1203094 520477 831818 970779 117955...   \n",
       "1  90540 816903 441039 816903 569138 816903 1034376 997489 382714 1236006 520477 132364 1207796 89613 1033299 834740 133940 816903 848000 386932 520477 31046 766772 748483 481786 701424 522428 764656...   \n",
       "\n",
       "   class  c_numerical  \\\n",
       "0     14            7   \n",
       "1      3            0   \n",
       "\n",
       "                                                                                                                                                                                                  word_mf2  \\\n",
       "0  816903 597526 520477 1179558 1033823 758724 632718 422098 520477 414956 625597 1203094 441513 596474 660569 995362 924085 1278762 1155376 1109510 823120 1275770 1203094 520477 831818 970779 117955...   \n",
       "1  90540 816903 441039 816903 569138 816903 1034376 997489 382714 1236006 520477 132364 1207796 89613 1033299 834740 133940 816903 848000 386932 520477 31046 766772 748483 481786 701424 522428 764656...   \n",
       "\n",
       "                                                                                                                                                                                                  char_mf2  \n",
       "0  7368 1252069 365865 755561 1044285 129532 105340 856005 1010033 679500 426236 755561 426236 697742 1044285 678940 1007781 1205281 263861 195449 862363 1274033 427848 1040756 427495 72195 222667 12...  \n",
       "1  581131 165432 7368 957317 1197553 570900 336592 7368 1030191 394259 230592 570900 7368 1155302 871922 94030 230478 1100129 1122654 538313 371412 313405 641588 952665 1044285 1043700 1007781 235615...  "
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 过滤低频词\n",
    "def filter_low_freq(df):\n",
    "    min_freq = 2\n",
    "    word_seg_mf2 = []\n",
    "    char_mf2 = []\n",
    "    for w in tqdm(df.word_seg):\n",
    "        word_seg_mf2.append(' '.join([e for e in w.split() if word_dict[e] > min_freq]))\n",
    "    for w in tqdm(df.article):\n",
    "        char_mf2.append(' '.join([e for e in w.split() if char_dict[e] > min_freq]))\n",
    "    df['word_mf2'] = word_seg_mf2\n",
    "    df['char_mf2'] = char_mf2\n",
    "filter_low_freq(train_df)\n",
    "train_df[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    {
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      "1899\t15\t0.8500444870303236\n",
      "1900\t12\t0.8501618154619358\n",
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      "1909\t13\t0.8514230961017669\n",
      "1910\t10\t0.851520869794777\n",
      "1911\t17\t0.8516870850728943\n",
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      "1913\t11\t0.8519217419361187\n",
      "1914\t10\t0.8520195156291288\n",
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      "1916\t13\t0.8522737272309553\n",
      "1917\t14\t0.8524106104011695\n",
      "1918\t22\t0.8526257125257919\n",
      "1919\t13\t0.8527528183267051\n",
      "1920\t13\t0.8528799241276184\n",
      "1921\t12\t0.8529972525592305\n",
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      "1925\t10\t0.8534567889163782\n",
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      "1927\t19\t0.8538478836884188\n",
      "1928\t10\t0.8539456573814289\n",
      "1929\t19\t0.8541314273981482\n",
      "1930\t7\t0.8541998689832553\n",
      "1931\t20\t0.8543954163692756\n",
      "1932\t13\t0.8545225221701889\n",
      "1933\t12\t0.854639850601801\n",
      "1934\t15\t0.8547865111413163\n",
      "1935\t14\t0.8549233943115305\n",
      "1936\t11\t0.8550309453738417\n",
      "1937\t12\t0.8551482738054539\n",
      "1938\t20\t0.8553438211914742\n",
      "1939\t11\t0.8554513722537854\n",
      "1940\t10\t0.8555491459467955\n",
      "1941\t13\t0.8556762517477088\n",
      "1942\t16\t0.855832689656525\n",
      "1943\t15\t0.8559793501960403\n",
      "1944\t7\t0.8560477917811474\n",
      "1945\t8\t0.8561260107355556\n",
      "1946\t13\t0.8562531165364689\n",
      "1947\t10\t0.856350890229479\n",
      "1948\t12\t0.8564682186610911\n",
      "1949\t15\t0.8566148792006064\n",
      "1950\t12\t0.8567322076322186\n",
      "1951\t12\t0.8568495360638307\n",
      "1952\t14\t0.856986419234045\n",
      "1953\t13\t0.8571135250349582\n",
      "1954\t11\t0.8572210760972694\n",
      "1955\t14\t0.8573579592674836\n",
      "1956\t9\t0.8574459555911927\n",
      "1957\t14\t0.857582838761407\n",
      "1958\t16\t0.8577392766702232\n",
      "1959\t21\t0.8579446014255445\n",
      "1960\t19\t0.8581303714422638\n",
      "1961\t15\t0.8582770319817791\n",
      "1962\t11\t0.8583845830440903\n",
      "1963\t13\t0.8585116888450035\n",
      "1964\t12\t0.8586290172766157\n",
      "1965\t8\t0.8587072362310239\n",
      "1966\t15\t0.8588538967705391\n",
      "1967\t10\t0.8589516704635493\n",
      "1968\t15\t0.8590983310030645\n",
      "1969\t16\t0.8592547689118808\n",
      "1970\t10\t0.8593525426048909\n",
      "1971\t11\t0.8594600936672021\n",
      "1972\t10\t0.8595578673602122\n",
      "1973\t12\t0.8596751957918244\n",
      "1974\t19\t0.8598609658085437\n",
      "1975\t15\t0.8600076263480589\n",
      "1976\t13\t0.8601347321489722\n",
      "1977\t20\t0.8603302795349925\n",
      "1978\t17\t0.8604964948131097\n",
      "1979\t15\t0.860643155352625\n",
      "1980\t22\t0.8608582574772474\n",
      "1981\t9\t0.8609462538009566\n",
      "1982\t16\t0.8611026917097728\n",
      "1983\t16\t0.8612591296185891\n",
      "1984\t18\t0.8614351222660074\n",
      "1985\t14\t0.8615720054362216\n",
      "1986\t13\t0.8616991112371348\n",
      "1987\t9\t0.861787107560844\n",
      "1988\t9\t0.8618751038845531\n",
      "1989\t10\t0.8619728775775632\n",
      "1990\t7\t0.8620413191626703\n",
      "1991\t6\t0.8620999833784765\n",
      "1992\t15\t0.8622466439179918\n",
      "1993\t9\t0.8623346402417009\n",
      "1994\t12\t0.8624519686733131\n",
      "1995\t12\t0.8625692971049252\n",
      "1996\t13\t0.8626964029058385\n",
      "1997\t10\t0.8627941765988486\n",
      "1998\t9\t0.8628821729225578\n",
      "1999\t6\t0.8629408371383639\n",
      "2000\t13\t0.8630679429392771\n",
      "2001\t14\t0.8632048261094913\n",
      "2002\t14\t0.8633417092797055\n",
      "2003\t9\t0.8634297056034147\n",
      "2004\t11\t0.8635372566657259\n",
      "2005\t16\t0.8636936945745421\n",
      "2006\t10\t0.8637914682675523\n",
      "2007\t13\t0.8639185740684655\n",
      "2008\t10\t0.8640163477614756\n",
      "2009\t11\t0.8641238988237868\n",
      "2010\t13\t0.8642510046247001\n",
      "2011\t14\t0.8643878877949143\n",
      "2012\t10\t0.8644856614879244\n",
      "2013\t9\t0.8645736578116335\n",
      "2014\t11\t0.8646812088739447\n",
      "2015\t12\t0.8647985373055569\n",
      "2016\t8\t0.8648767562599651\n",
      "2017\t9\t0.8649647525836742\n",
      "2018\t17\t0.8651309678617914\n",
      "2019\t11\t0.8652385189241026\n",
      "2020\t18\t0.8654145115715209\n",
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      "2026\t11\t0.8660891500532911\n",
      "2027\t15\t0.8662358105928064\n",
      "2028\t14\t0.8663726937630206\n",
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      "2039\t8\t0.8675948649256475\n",
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      "2100\t12\t0.8752212129804395\n",
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    },
    {
     "data": {
      "text/plain": [
       "102277"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def check_len(df):\n",
    "    s_dct = {}\n",
    "    for s in df.article:\n",
    "        s = [e for e in s.split() if e not in char_stop_word]\n",
    "        s_dct[len(s)] = s_dct.get(len(s), 0) + 1\n",
    "    l_lst = sorted(s_dct.items(), key=lambda x:x[0], reverse=False)\n",
    "    sum(s_dct.values())\n",
    "    p = 0.\n",
    "    for l in l_lst:\n",
    "        p += l[1] / sum(s_dct.values())\n",
    "        print(str(l[0]) + '\\t' + str(l[1]) + '\\t' + str(p))\n",
    "check_len(train_df)\n",
    "# check_len(test_df)\n",
    "len(train_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "model_id": "1ef70c3e5e604fabb0182d00780c0075",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=204554), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "训练集语料: 204554\n",
      "总长度:  204554\n",
      "保存模型...\n"
     ]
    },
    {
     "data": {
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       "model_id": "7d7855cd141744a1b0c4bd0e95b28d88",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=10528), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "dccade173cd0467a965f7dd656fed05e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=204554), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "训练集语料: 204554\n",
      "总长度:  204554\n",
      "保存模型...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "741cfad3480047b58b62d37417679b02",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
      ],
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=359278), HTML(value='')))"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "OK\n"
     ]
    }
   ],
   "source": [
    "# 训练词向量\n",
    "def train_w2v_model(type='article', min_freq=5, size=100):\n",
    "    sentences = []\n",
    "\n",
    "    if type == 'char':\n",
    "        corpus = pd.concat((train_df['article'], test_df['article']))\n",
    "    elif type == 'word':\n",
    "        corpus = pd.concat((train_df['word_seg'], test_df['word_seg']))\n",
    "    for e in tqdm(corpus):\n",
    "        sentences.append([i for i in e.strip().split() if i])\n",
    "    print('训练集语料:', len(corpus))\n",
    "    print('总长度: ', len(sentences))\n",
    "    model = Word2Vec(sentences, size=size, window=5, min_count=min_freq)\n",
    "    model.itos = {}\n",
    "    model.stoi = {}\n",
    "    model.embedding = {}\n",
    "    \n",
    "    print('保存模型...')\n",
    "    for k in tqdm(model.wv.vocab.keys()):\n",
    "        model.itos[model.wv.vocab[k].index] = k\n",
    "        model.stoi[k] = model.wv.vocab[k].index\n",
    "        model.embedding[model.wv.vocab[k].index] = model.wv[k]\n",
    "\n",
    "    model.save('../../data/word2vec-models/word2vec.{}.{}d.mfreq{}.model'.format(type, size, min_freq))\n",
    "    return model\n",
    "model = train_w2v_model(type='char', size=100)\n",
    "model = train_w2v_model(type='word', size=100)\n",
    "# model.wv.save_word2vec_format('../../data/laozhu-word-300d', binary=False)\n",
    "# train_df[:3]\n",
    "print('OK')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "256"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 训练tf-idf特征\n",
    "os.system('python train_lsa.py char 250')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>article</th>\n",
       "      <th>word_seg</th>\n",
       "      <th>class</th>\n",
       "      <th>c_numerical</th>\n",
       "      <th>word_mf2</th>\n",
       "      <th>char_mf2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>7368 1252069 365865 755561 1044285 129532 105340 856005 1010033 679500 426236 755561 426236 697742 1044285 678940 1007781 1205281 263861 195449 862363 1274033 427848 1040756 427495 72195 222667 12...</td>\n",
       "      <td>816903 597526 520477 1179558 1033823 758724 632718 422098 520477 414956 625597 1203094 441513 596474 660569 995362 924085 1278762 1155376 1109510 823120 1275770 1203094 520477 831818 970779 117955...</td>\n",
       "      <td>14</td>\n",
       "      <td>7</td>\n",
       "      <td>816903 597526 520477 1179558 1033823 758724 632718 422098 520477 414956 625597 1203094 441513 596474 660569 995362 924085 1278762 1155376 1109510 823120 1275770 1203094 520477 831818 970779 117955...</td>\n",
       "      <td>7368 1252069 365865 755561 1044285 129532 105340 856005 1010033 679500 426236 755561 426236 697742 1044285 678940 1007781 1205281 263861 195449 862363 1274033 427848 1040756 427495 72195 222667 12...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  \\\n",
       "0   0   \n",
       "\n",
       "                                                                                                                                                                                                   article  \\\n",
       "0  7368 1252069 365865 755561 1044285 129532 105340 856005 1010033 679500 426236 755561 426236 697742 1044285 678940 1007781 1205281 263861 195449 862363 1274033 427848 1040756 427495 72195 222667 12...   \n",
       "\n",
       "                                                                                                                                                                                                  word_seg  \\\n",
       "0  816903 597526 520477 1179558 1033823 758724 632718 422098 520477 414956 625597 1203094 441513 596474 660569 995362 924085 1278762 1155376 1109510 823120 1275770 1203094 520477 831818 970779 117955...   \n",
       "\n",
       "   class  c_numerical  \\\n",
       "0     14            7   \n",
       "\n",
       "                                                                                                                                                                                                  word_mf2  \\\n",
       "0  816903 597526 520477 1179558 1033823 758724 632718 422098 520477 414956 625597 1203094 441513 596474 660569 995362 924085 1278762 1155376 1109510 823120 1275770 1203094 520477 831818 970779 117955...   \n",
       "\n",
       "                                                                                                                                                                                                  char_mf2  \n",
       "0  7368 1252069 365865 755561 1044285 129532 105340 856005 1010033 679500 426236 755561 426236 697742 1044285 678940 1007781 1205281 263861 195449 862363 1274033 427848 1040756 427495 72195 222667 12...  "
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 保存训练数据\n",
    "train_df.to_csv('../../data/New.train.csv')\n",
    "test_df.to_csv('../../data/New.test.csv')\n",
    "train_df[:1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# $$三、数据增强（option）$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 随机数添加数据\n",
    "import random\n",
    "\n",
    "def run_enhance():\n",
    "    max_len = train_df['class'].value_counts().values[0]\n",
    "    enhance_df = train_df.copy()[['article', 'word_seg', 'class', 'c_numerical']]\n",
    "    \n",
    "    for c in tqdm(enhance_df['class'].value_counts().index):\n",
    "        c_data = enhance_df[enhance_df['class'] == c]\n",
    "        if len(c_data) * 2 < max_len:\n",
    "            for a, b, c_n in zip(c_data['word_seg'].values, c_data['article'].values, c_data['c_numerical'].values):\n",
    "                a_lst = a.split()\n",
    "                b_lst = b.split()\n",
    "                random.shuffle(a_lst)\n",
    "                random.shuffle(b_lst)\n",
    "                a_str = ' '.join(a_lst)\n",
    "                b_str = ' '.join(b_lst)\n",
    "                enhance_df.loc[enhance_df.shape[0]+1] = {'article': b_str, 'word_seg': a_str, 'class': c, 'c_numerical':c_n}\n",
    "    return enhance_df\n",
    "enhance_df = run_enhance()\n",
    "enhance_df.to_csv('../../data/Enhance.train.csv')\n",
    "enhance_df['class'].value_counts().plot.bar(figsize=(10,6), fontsize=20, rot=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 卡方统计量提取数据\n",
    "chi_dic = {}\n",
    "enhance_df[:1]\n",
    "\n",
    "N = len(train_df)\n",
    "\n",
    "all_word = set((' '.join(train_df['word_seg'])).split())\n",
    "all_char = set((' '.join(train_df['article'])).split())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "类别列表id： {0: 0, 1: 1, 2: 2, 3: 3, 4: 4, 5: 5, 6: 6, 7: 7, 8: 8, 9: 9, 10: 10, 11: 11, 12: 12, 13: 13, 14: 14, 15: 15, 16: 16, 17: 17, 18: 18}\n",
      "文本总数量： 102277\n",
      "每个类别的词数量： [132084. 181117. 124317. 106710. 266871. 162623. 108528. 130569. 221148.\n",
      " 131776. 117768. 129654.  90460.  70151.  84262.  99127.  93798. 128804.\n",
      "  67316.]\n",
      "总词数： 875129\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/data3/jzzhou/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:51: RuntimeWarning: divide by zero encountered in true_divide\n",
      "/data3/jzzhou/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:51: RuntimeWarning: invalid value encountered in true_divide\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "提取的特征词的数量： 560495\n",
      "类别列表id： {0: 0, 1: 1, 2: 2, 3: 3, 4: 4, 5: 5, 6: 6, 7: 7, 8: 8, 9: 9, 10: 10, 11: 11, 12: 12, 13: 13, 14: 14, 15: 15, 16: 16, 17: 17, 18: 18}\n",
      "文本总数量： 102277\n",
      "每个类别的词数量： [6502. 6975. 6728. 6143. 9469. 6038. 5599. 5794. 8321. 6014. 6634. 5703.\n",
      " 5276. 6083. 4968. 6067. 5806. 6502. 4629.]\n",
      "总词数： 13516\n",
      "提取的特征词的数量： 13516\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "def Feature_Select(df, mode='chi', feature='word', num=1000):  #  chi mi ig df ts\n",
    "    all_text = []\n",
    "    label_set = set()  # 'corpus_txt'\n",
    "    feature = 'word_seg' if feature == 'word' else 'article'\n",
    "    for idx, line in df.iterrows():\n",
    "        data = line[feature].strip().split()\n",
    "        all_text.append((line['c_numerical'], data))\n",
    "        label_set.add(line['c_numerical'])\n",
    "    categories = list(sorted(label_set))  # 排序，确保每次生成的类别列表顺序唯一\n",
    "    cat_to_id = dict(zip(categories, range(len(categories))))\n",
    "    print(\"类别列表id：\", cat_to_id)\n",
    "\n",
    "    word_text_map = {}\n",
    "    label_num = [0] * len(categories)\n",
    "    for text in all_text:\n",
    "        label_i = cat_to_id[text[0]]\n",
    "        label_num[label_i] += 1\n",
    "\n",
    "        t = sorted(set(text[1]))  # 去重，并保持原来顺序，不保持原序结果不一样\n",
    "        for w in t:\n",
    "            if w not in word_text_map:\n",
    "                word_text_map[w] = [0] * len(categories)\n",
    "                word_text_map[w][label_i] += 1\n",
    "            else:\n",
    "                word_text_map[w][label_i] += 1\n",
    "\n",
    "    N = len(all_text)\n",
    "    print('文本总数量：', N)\n",
    "\n",
    "    word_text_list = []\n",
    "    word_list = []\n",
    "    for k in word_text_map:\n",
    "        word_list.append(k)\n",
    "        word_text_list.append(word_text_map[k])\n",
    "    \n",
    "    A_array = np.array(word_text_list, dtype=np.float)  # A\n",
    "    sign_array = np.sign(A_array)  # 当A=0时，chi=0\n",
    "    word_num_each_label = np.sum(sign_array, 0)\n",
    "    print('每个类别的词数量：', word_num_each_label)\n",
    "    label_num = np.array(label_num, dtype=np.float)\n",
    "    B_array = np.reshape(np.sum(word_text_list, 1), (-1, 1)) - A_array\n",
    "    C_array = label_num - A_array\n",
    "    D_array = N - A_array - B_array - C_array\n",
    "    word_num = len(word_list)\n",
    "    print('总词数：', word_num)\n",
    "\n",
    "    def chi_label():\n",
    "        # N * (AD - BC)^2  /  ((A+C)(B+D) (A+B)(C+D))   # 简化版 对于同一个类别：(AD - BC)^2  /  ((A+B)(C+D))\n",
    "        chi_square_value = (A_array*D_array - B_array*C_array) ** 2 / ((A_array+B_array) * (C_array+D_array))\n",
    "        # chi_square_value = (A_array*D_array - B_array*C_array) ** 2 * N / (\n",
    "        #                         (A_array+C_array)*(B_array+D_array)*(A_array+B_array)*(C_array+D_array))\n",
    "        chi_square_value = sign_array * chi_square_value\n",
    "        \n",
    "        feature_word = {}\n",
    "        for i in range(len(categories)):\n",
    "            cur_chi = chi_square_value[:, i]\n",
    "            order = np.argsort(cur_chi)  # 返回排序后的索引，正向排序\n",
    "            # print(word_num_each_label[i])\n",
    "            min_n = min(num, int(word_num_each_label[i]))\n",
    "            # top_80 = int(word_num_each_label[i] * 0.5)\n",
    "            for i in order[-min_n:]:\n",
    "                feature_word[i] = 1\n",
    "        chose_w = [word_list[i] for i in feature_word]\n",
    "        print('提取的特征词的数量：', len(chose_w))\n",
    "        return chose_w\n",
    "    \n",
    "    def chi_mode(mode):\n",
    "        # N * (AD - BC)^2  /  ((A+C)(B+D) (A+B)(C+D))   \n",
    "        chi_square_value = (A_array*D_array - B_array*C_array) ** 2 * N / (\n",
    "                                (A_array+C_array)*(B_array+D_array)*(A_array+B_array)*(C_array+D_array))\n",
    "        chi_square_value = sign_array * chi_square_value\n",
    "        \n",
    "        if mode == 'max': chi_square_value = np.max(chi_square_value, axis=1).reshape((-1, 1))\n",
    "        elif mode == 'avg': chi_square_value = np.mean(chi_square_value, axis=1).reshape((-1, 1))\n",
    "        else: raise ValueError(\"参数有误\")\n",
    "\n",
    "        order = np.argsort(chi_square_value[:, 0])  # 返回排序后的索引，正向排序\n",
    "        chose_w = [word_list[i] for i in order[-num:]]\n",
    "        return chose_w\n",
    "    return chi_label()\n",
    "w_num = 100000\n",
    "c_num = 20000\n",
    "w = Feature_Select(train_df, num=num, feature='word')\n",
    "c = Feature_Select(train_df, num=num, feature='char')\n",
    "pickle.dump(w, open('../../data/chi_words_{}.pkl'.format(w_num), 'wb'))\n",
    "pickle.dump(c, open('../../data/chi_char_{}.pkl'.format(c_num), 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred1 = pickle.load(open('../../data/result-op5/lstmgruword_oof_f1_0.7686691377795438_a0.7832944028205417.pkl', 'rb'))\n",
    "pred2 = pickle.load(open('../../backup/result-op3-0907/svmword_pre_f1_0.7755615966138534_a0.7900114681760386.pkl', 'rb'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "model_id": "9b45db164393402989aa103d7510f16e",
       "version_major": 2,
       "version_minor": 0
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      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=102277), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b6af2e7e053d43f1b5b81a61118f5c49",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=102277), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "16464\n",
      "26207\n"
     ]
    }
   ],
   "source": [
    "def get_noise(pred):\n",
    "    lines = []\n",
    "    for i, p in enumerate(tqdm(pred)):\n",
    "        top2 = np.sort(p)[-2:]\n",
    "        if top2[0] >= 0.15 and top2[1] <= 0.7:\n",
    "            lines.append(i)\n",
    "    return lines\n",
    "line1 = set(get_noise(pred1))\n",
    "line2 = set(get_noise(pred2))\n",
    "print(len(line1))\n",
    "print(len(line2))\n",
    "cross_noise = line1 & line2\n",
    "cross_noise = list(cross_noise)"
   ]
  }
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